Exploiting Self-Adjusted Logical Individual Feature Subspace for Hyperspectral Analysis
Bibliographic record
Abstract
It is critical to decompose mixed pixels in ahyperspectral image (HSI)into pure spectral signatures and fractions, known as endmembers and abundances. However, current methods usually combine all endmembers and abundances into a single matrix. However, this approach overlooks the distinct capacity differences of each substance subspace. Furthermore, traditional approaches typically reconstruct without accounting for corresponding errors, resulting in suboptimal outcomes. In this work, we introduce a novel framework that uses self-adjustedindividual logical feature (LIF)subspaces for each substance. This enables the accurate modeling of each substance’s unique properties. Our method calculates the capacity of each subspace by unifying the features of each substance, thereby ensuring a more accurate representation. Importantly, our approach balances reconstruction fidelity and error, preventing blind approximation of the observed HSI and addressing overfitting. Additionally, our approach exploits correlations between reconstructed subspaces to minimize redundancy. Extensive experimental results on several datasets demonstrate the superior performance and validity of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".